{"id":"https://openalex.org/W4389495275","doi":"https://doi.org/10.1109/twc.2023.3337058","title":"Bayesian Inference-Assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)","display_name":"Bayesian Inference-Assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)","publication_year":2023,"publication_date":"2023-12-08","ids":{"openalex":"https://openalex.org/W4389495275","doi":"https://doi.org/10.1109/twc.2023.3337058"},"language":"en","primary_location":{"id":"doi:10.1109/twc.2023.3337058","is_oa":false,"landing_page_url":"https://doi.org/10.1109/twc.2023.3337058","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Wireless Communications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077715225","display_name":"Shashank Jere","orcid":"https://orcid.org/0000-0001-6451-253X"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shashank Jere","raw_affiliation_strings":["Bradley Department of ECE, Wireless&#x0040;Virgnia Tech, Virginia Tech, Blacksburg, VA, USA"],"raw_orcid":"https://orcid.org/0000-0001-6451-253X","affiliations":[{"raw_affiliation_string":"Bradley Department of ECE, Wireless&#x0040;Virgnia Tech, Virginia Tech, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018979762","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0002-9004-7253"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-9004-7253","affiliations":[{"raw_affiliation_string":"School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059786503","display_name":"Ishan Aryendu","orcid":"https://orcid.org/0000-0003-4340-565X"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ishan Aryendu","raw_affiliation_strings":["School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0003-4340-565X","affiliations":[{"raw_affiliation_string":"School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070503414","display_name":"Shehadi Dayekh","orcid":"https://orcid.org/0009-0007-7693-6373"},"institutions":[{"id":"https://openalex.org/I145325580","display_name":"Deloitte (United States)","ror":"https://ror.org/03xkm6e60","country_code":"US","type":"company","lineage":["https://openalex.org/I145325580","https://openalex.org/I4210139068"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shehadi Dayekh","raw_affiliation_strings":["5G, Advanced Connectivity and Edge Cyber, Deloitte &#x0026; Touche LLP, Dallas, TX, USA"],"raw_orcid":"https://orcid.org/0009-0007-7693-6373","affiliations":[{"raw_affiliation_string":"5G, Advanced Connectivity and Edge Cyber, Deloitte &#x0026; Touche LLP, Dallas, TX, USA","institution_ids":["https://openalex.org/I145325580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027237940","display_name":"Lingjia Liu","orcid":"https://orcid.org/0000-0003-1915-1784"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lingjia Liu","raw_affiliation_strings":["Bradley Department of ECE, Wireless&#x0040;Virgnia Tech, Virginia Tech, Blacksburg, VA, USA"],"raw_orcid":"https://orcid.org/0000-0003-1915-1784","affiliations":[{"raw_affiliation_string":"Bradley Department of ECE, Wireless&#x0040;Virgnia Tech, Virginia Tech, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.9159,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.94923697,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"23","issue":"7","first_page":"7043","last_page":"7059"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11996","display_name":"Random lasers and scattering media","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/3102","display_name":"Acoustics and Ultrasonics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7604283094406128},{"id":"https://openalex.org/keywords/jamming","display_name":"Jamming","score":0.6642993688583374},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6218358278274536},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5669469237327576},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5385969281196594},{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.4688614010810852},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4620133936405182},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.4543461501598358},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4087076783180237},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3952085077762604},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37294691801071167},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.0810244083404541}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7604283094406128},{"id":"https://openalex.org/C2779079576","wikidata":"https://www.wikidata.org/wiki/Q17092823","display_name":"Jamming","level":2,"score":0.6642993688583374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6218358278274536},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5669469237327576},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5385969281196594},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.4688614010810852},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4620133936405182},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.4543461501598358},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4087076783180237},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3952085077762604},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37294691801071167},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0810244083404541},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/twc.2023.3337058","is_oa":false,"landing_page_url":"https://doi.org/10.1109/twc.2023.3337058","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Wireless Communications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2578973671","https://openalex.org/W2215058820","https://openalex.org/W2097663773","https://openalex.org/W1602184117","https://openalex.org/W2413421635","https://openalex.org/W2511198839","https://openalex.org/W1966557338","https://openalex.org/W2366931106","https://openalex.org/W1941202037","https://openalex.org/W4233612155"],"abstract_inverted_index":{"The":[0,71,115,160],"increased":[1],"flexibility":[2],"and":[3,17,27,63,78,93,141,195],"density":[4],"of":[5,125,170,174],"spectrum":[6],"access":[7],"in":[8,167,192],"5G":[9,52,193],"New":[10],"Radio":[11],"(NR)":[12],"has":[13],"made":[14],"jamming":[15,26,113,128,148],"detection":[16,119,149,178],"classification":[18],"a":[19,32,49,138,142],"critical":[20],"research":[21],"area.":[22],"To":[23],"detect":[24],"coexisting":[25],"subtle":[28],"interference,":[29],"we":[30],"introduce":[31],"Bayesian":[33,66],"Inference-assisted":[34],"machine":[35],"learning":[36,58],"(ML)":[37],"methodology.":[38],"Our":[39,134],"methodology":[40],"uses":[41],"cross-layer":[42],"Key":[43],"Performance":[44],"Indicator":[45],"data":[46,81,184],"collected":[47],"on":[48,75],"Non-Standalone":[50],"(NSA)":[51],"NR":[53,194],"testbed":[54],"to":[55,131],"leverage":[56],"supervised":[57],"models,":[59],"further":[60],"assessed,":[61],"calibrated,":[62],"revealed":[64],"using":[65],"Network":[67],"Model":[68],"(BNM)-based":[69],"inference.":[70],"models":[72,92,98],"can":[73],"operate":[74],"both":[76],"instantaneous":[77,91,118],"sequential":[79,97,177],"time-series":[80],"samples,":[82,185],"achieving":[83],"an":[84],"Area":[85],"under":[86],"Curve":[87],"above":[88,94],"0.954":[89],"for":[90,96,122,146,156],"0.988":[95],"including":[99],"the":[100,106,126,171,175],"echo":[101],"state":[102],"network":[103],"(ESN)":[104],"from":[105],"Reservoir":[107],"Computing":[108],"(RC)":[109],"family,":[110],"across":[111],"various":[112],"scenarios.":[114],"180":[116],"ms":[117],"time":[120],"allows":[121],"continuous":[123],"tracking":[124],"dynamic":[127],"condition":[129],"due":[130],"UE":[132],"mobility.":[133],"approach":[135],"serves":[136],"as":[137],"validation":[139],"method":[140],"resilience":[143],"enhancement":[144],"tool":[145],"ML-based":[147],"while":[150],"also":[151],"enabling":[152],"root":[153],"cause":[154],"identification":[155],"observed":[157],"performance":[158],"degradation.":[159],"introduced":[161],"BNM-based":[162],"inference":[163],"proof-of-concept":[164],"is":[165],"successful":[166],"addressing":[168],"72.2%":[169],"erroneous":[172],"predictions":[173],"RC-based":[176],"model":[179],"caused":[180],"by":[181],"insufficient":[182],"training":[183],"thereby":[186],"demonstrating":[187],"its":[188],"near":[189],"real-time":[190],"applicability":[191],"Beyond-5G":[196],"networks.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
